25 August 2004 A novel MCMC tracker for stressing scenarios
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Abstract
We propose a very generic Bayesian framework for the principled exploitation of probabilistic batch-learning technologies for real-time state estimation. To illustrate our concepts, we derive a nonlinear filtering/smoothing solution for a challenging case study in target tracking. We also demonstrate the application of Markov chain Monte Carlo (MCMC) sampling methods as a computational tool within our framework. Finally, we present simulation results, benchmarked against a comparable particle filter.
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Nick Everett, Nick Everett, Shien-Shin Tham, Shien-Shin Tham, David J. Salmond, David J. Salmond, } "A novel MCMC tracker for stressing scenarios", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.541583; https://doi.org/10.1117/12.541583
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